Komparasi Algoritma Klasifikasi Machine Learning Dengan Penerapan Metode Ensemble Stacking untuk Menganalisa Sentimen terhadap Kesehatan Mental

Annisa Maulana Majid
Karina Imelda
Ismasari Nawangsih

SKANIKA: Sistem Komputer dan Teknik Informatika
Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025

Mental health often goes undetected due to the absence of physical symptoms, which hinders timely and appropriate intervention. Many individuals choose to express their emotions on social media rather than access professional services. However, the use of social media can potentially worsen mental health conditions and even impact physical well-being. Therefore, early detection through the analysis of digital data, particularly social media posts, using machine learning approaches is essential. Previous research on mental health sentiment analysis has utilized classification algorithms, but accuracy improvement remains necessary. This study compares single classification algorithms and applies an ensemble stacking method that combines multiple classifiers as base learners and a meta-learner. The results show that the stacking method achieves a higher accuracy of 88.13%.